检索结果-南通市图书馆

network traffic prediction by a wavelet-based combined model

Chinese Physics B 2009年第11期18卷 4760-4768页

作者：孙韩林金跃辉崔毅东程时端 State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications School of Information and Telecommunication Engineering Beijing University of Posts and Telecommunications

network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet-grey-chaos （WGC）, for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard à trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM（1,1） and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.

关键词： network traffic prediction wavelet transform grey model chaos model

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A network traffic prediction Algorithm Based on Prophet-EALSTM-GPR

引用

Journal on Internet of Things 2022年第2期4卷 113-125页

作者： Guoqing Xu Changsen Xia Jun Qian Guo Ran Zilong Jin School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing210044China Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou510006China

Huge networks and increasing network traffic will consume more and more *** is critical to predict network traffic accurately and timely for network planning,and resource allocation,*** this paper,a combined network traffic prediction model is proposed,which is based on Prophet,evolutionary attention-based LSTM(EALSTM)network,and Gaussian process regression(GPR).According to the non-smooth,sudden,periodic,and long correlation characteristics of network traffic,the prediction procedure is divided into three steps to predict network traffic *** the first step,the Prophetmodel decomposes network traffic data into periodic and non-periodic *** periodic term is predicted by the Prophet model for different granularity *** the second step,the non-periodic term is fed to an EALSTM network to extract the importance of the different features in the sequence and learn their long correlation,which effectively avoids the long-term dependence problem caused by long step ***,GPR is used to predict the residual term to boost the predictability even *** results indicate that the proposed scheme is more applicable and can significantly improve prediction accuracy compared with traditional linear and nonlinear models.

关键词： network traffic prediction prophet EALSTM gaussian process regression

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

prediction Method for network traffic Based on Maximum Correntropy Criterion

引用

China Communications 2013年第1期10卷 134-145页

作者：曲桦马文涛赵季红王涛 School of Electronic and Information Engineering Xi'an Jiaotong University School of Telecommunication and Information Engineering Xi'an University of Posts and Telecommunications

This paper proposes a method for improving the precision of network traffic prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are *** method utilizes the MCC as a new error evaluation criterion or named the cost function(CF)to train neural networks(NN).MCC is based on a new similarity function(Generalized correlation entropy function,Correntropy),which has as its foundation the Parzen window evaluation and Renyi entropy of error probability density *** the same time,by combining the MCC with the Mean Square Error(MSE),a mixed evaluation criterion with MCC and MSE is proposed as a cost function of NN *** to the traffic network characteristics including the nonlinear,non-Gaussian,and mutation,the Elman neural network is trained by MCC and MCC-MSE,and then the trained neural network is used as the model for predicting network *** simulation results based on the evaluation by Mean Absolute Error(MAE),MSE,and Sum Squared Error(SSE)show that the accuracy of the prediction based on MCC is superior to the results of the Elman neural network with *** overall performance is improved by about 0.0131.

关键词： MCC MSE Elman neural net-work network traffic prediction

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Attention-Based Multi-Scale prediction network for Time-Series Data

引用

China Communications 2022年第5期19卷 286-301页

作者： Junjie Li Lin Zhu Yong Zhang Da Guo Xingwen Xia School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Key Laboratory of Work Safety Intelligent Monitoring(Beijing University of Posts and Telecommunications) Beijing 100876China China Mobile Research Institute Beijing 100053China

Time series data is a kind of data accumulated over time,which can describe the change of *** kind of data reflects the degree of change of a certain thing or *** existing technologies such as LSTM and ARIMA are better than convolutional neural network in time series prediction,but they are not enough to mine the periodicity of *** this article,we perform periodic analysis on two types of time series data,select time metrics with high periodic characteristics,and propose a multi-scale prediction model based on the attention mechanism for the periodic trend of the data.A loss calculation method for traffic time series characteristics is proposed as *** experiments have been conducted on actual data *** experiments show that the method proposed in this paper has better performance than commonly used traffic prediction methods(ARIMA,LSTM,etc.)and 3%-5%increase on MAPE.

关键词： network traffic prediction attention mechanism neural network machine learning single point forecast

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Joint network traffic Forecast with ARIMA models and Chaotic Models Based on Wavelet Analysis

Joint Network Traffic Forecast with ARIMA models and Chaotic...

引用

The International Conference on Recent Trends in Materials and Mechanical Engineering (ICRTMME 2011)

作者： Mingke Dong Chen Chen Minhua Huang Ye Jin School of Electronics Engineering and Computer Science Peking University

In the recent study of network traffic,it is shown that the traffic flow presents both periodic and self-similar *** to these two features,the short-term forecast of network traffic cannot be accurately fit in either autoregressive integrated moving average(ARIMA) models which is suitable for linear behavior,or chaotic models which is corresponding to self-similarity *** this paper,our methodology suggests that by using wavelet multiresolution analysis,we can obtain a joint short-term network traffic prediction method and get a more precise forecast result as compared to using either ARIMA models or chaotic *** also run simulations to show the improvement of prediction accuracy of our proposed approach.

关键词： network traffic prediction chaotic prediction wavelet analysis

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：